Overview

Dataset statistics

Number of variables18
Number of observations10970
Missing cells3693
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 MiB
Average record size in memory420.4 B

Variable types

Numeric10
Categorical8

Alerts

ratio has 2581 (23.5%) missing valuesMissing
region has 1090 (9.9%) missing valuesMissing
clientid is uniformly distributedUniform
clientid has unique valuesUnique
ratio has 578 (5.3%) zerosZeros
income has 2770 (25.3%) zerosZeros

Reproduction

Analysis started2024-05-02 15:56:04.320173
Analysis finished2024-05-02 15:56:34.052002
Duration29.73 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

clientid
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct10970
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5484.5
Minimum0
Maximum10969
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size85.8 KiB
2024-05-02T17:56:34.328748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile548.45
Q12742.25
median5484.5
Q38226.75
95-th percentile10420.55
Maximum10969
Range10969
Interquartile range (IQR)5484.5

Descriptive statistics

Standard deviation3166.9106
Coefficient of variation (CV)0.57742922
Kurtosis-1.2
Mean5484.5
Median Absolute Deviation (MAD)2742.5
Skewness0
Sum60164965
Variance10029322
MonotonicityStrictly increasing
2024-05-02T17:56:34.780445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
7316 1
 
< 0.1%
7308 1
 
< 0.1%
7309 1
 
< 0.1%
7310 1
 
< 0.1%
7311 1
 
< 0.1%
7312 1
 
< 0.1%
7313 1
 
< 0.1%
7314 1
 
< 0.1%
7315 1
 
< 0.1%
Other values (10960) 10960
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
10969 1
< 0.1%
10968 1
< 0.1%
10967 1
< 0.1%
10966 1
< 0.1%
10965 1
< 0.1%
10964 1
< 0.1%
10963 1
< 0.1%
10962 1
< 0.1%
10961 1
< 0.1%
10960 1
< 0.1%

client_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size670.3 KiB
early
7892 
regular
1771 
default
1307 

Length

Max length7
Median length5
Mean length5.5611668
Min length5

Characters and Unicode

Total characters61006
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdefault
2nd rowdefault
3rd rowdefault
4th rowdefault
5th rowdefault

Common Values

ValueCountFrequency (%)
early 7892
71.9%
regular 1771
 
16.1%
default 1307
 
11.9%

Length

2024-05-02T17:56:35.187095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-02T17:56:35.483877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
early 7892
71.9%
regular 1771
 
16.1%
default 1307
 
11.9%

Most occurring characters

ValueCountFrequency (%)
r 11434
18.7%
e 10970
18.0%
a 10970
18.0%
l 10970
18.0%
y 7892
12.9%
u 3078
 
5.0%
g 1771
 
2.9%
d 1307
 
2.1%
f 1307
 
2.1%
t 1307
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61006
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 11434
18.7%
e 10970
18.0%
a 10970
18.0%
l 10970
18.0%
y 7892
12.9%
u 3078
 
5.0%
g 1771
 
2.9%
d 1307
 
2.1%
f 1307
 
2.1%
t 1307
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61006
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 11434
18.7%
e 10970
18.0%
a 10970
18.0%
l 10970
18.0%
y 7892
12.9%
u 3078
 
5.0%
g 1771
 
2.9%
d 1307
 
2.1%
f 1307
 
2.1%
t 1307
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61006
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 11434
18.7%
e 10970
18.0%
a 10970
18.0%
l 10970
18.0%
y 7892
12.9%
u 3078
 
5.0%
g 1771
 
2.9%
d 1307
 
2.1%
f 1307
 
2.1%
t 1307
 
2.1%

ratio
Real number (ℝ)

MISSING  ZEROS 

Distinct183
Distinct (%)2.2%
Missing2581
Missing (%)23.5%
Infinite0
Infinite (%)0.0%
Mean0.2734627
Minimum0
Maximum4.1666667
Zeros578
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size85.8 KiB
2024-05-02T17:56:36.226985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.066666667
median0.16666667
Q30.375
95-th percentile0.95833333
Maximum4.1666667
Range4.1666667
Interquartile range (IQR)0.30833333

Descriptive statistics

Standard deviation0.29338163
Coefficient of variation (CV)1.0728397
Kurtosis5.7542231
Mean0.2734627
Median Absolute Deviation (MAD)0.125
Skewness1.7911874
Sum2294.0786
Variance0.08607278
MonotonicityNot monotonic
2024-05-02T17:56:36.619101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 578
 
5.3%
0.08333333333 577
 
5.3%
0.1666666667 458
 
4.2%
0.25 326
 
3.0%
0.3333333333 239
 
2.2%
0.04166666667 229
 
2.1%
0.05555555556 221
 
2.0%
0.125 213
 
1.9%
0.02777777778 208
 
1.9%
0.1111111111 194
 
1.8%
Other values (173) 5146
46.9%
(Missing) 2581
23.5%
ValueCountFrequency (%)
0 578
5.3%
0.008333333333 9
 
0.1%
0.01041666667 2
 
< 0.1%
0.0119047619 25
 
0.2%
0.01388888889 10
 
0.1%
0.01666666667 165
 
1.5%
0.02040816327 1
 
< 0.1%
0.02083333333 84
 
0.8%
0.02380952381 22
 
0.2%
0.025 8
 
0.1%
ValueCountFrequency (%)
4.166666667 1
 
< 0.1%
2.583333333 1
 
< 0.1%
2.333333333 1
 
< 0.1%
2.083333333 1
 
< 0.1%
2 1
 
< 0.1%
1.666666667 1
 
< 0.1%
1.583333333 1
 
< 0.1%
1.5 3
< 0.1%
1.416666667 3
< 0.1%
1.333333333 7
0.1%

loan_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size704.3 KiB
leaseback
9509 
leasing
1461 

Length

Max length9
Median length9
Mean length8.7336372
Min length7

Characters and Unicode

Total characters95808
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowleaseback
2nd rowleaseback
3rd rowleasing
4th rowleaseback
5th rowleasing

Common Values

ValueCountFrequency (%)
leaseback 9509
86.7%
leasing 1461
 
13.3%

Length

2024-05-02T17:56:36.961880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-02T17:56:37.223285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
leaseback 9509
86.7%
leasing 1461
 
13.3%

Most occurring characters

ValueCountFrequency (%)
e 20479
21.4%
a 20479
21.4%
l 10970
11.4%
s 10970
11.4%
b 9509
9.9%
c 9509
9.9%
k 9509
9.9%
i 1461
 
1.5%
n 1461
 
1.5%
g 1461
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 95808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 20479
21.4%
a 20479
21.4%
l 10970
11.4%
s 10970
11.4%
b 9509
9.9%
c 9509
9.9%
k 9509
9.9%
i 1461
 
1.5%
n 1461
 
1.5%
g 1461
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 95808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 20479
21.4%
a 20479
21.4%
l 10970
11.4%
s 10970
11.4%
b 9509
9.9%
c 9509
9.9%
k 9509
9.9%
i 1461
 
1.5%
n 1461
 
1.5%
g 1461
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 95808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 20479
21.4%
a 20479
21.4%
l 10970
11.4%
s 10970
11.4%
b 9509
9.9%
c 9509
9.9%
k 9509
9.9%
i 1461
 
1.5%
n 1461
 
1.5%
g 1461
 
1.5%

loan_initial_term
Real number (ℝ)

Distinct35
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.818323
Minimum9
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.8 KiB
2024-05-02T17:56:37.480157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile18
Q136
median54
Q390
95-th percentile108
Maximum180
Range171
Interquartile range (IQR)54

Descriptive statistics

Standard deviation28.195104
Coefficient of variation (CV)0.45609623
Kurtosis0.87781578
Mean61.818323
Median Absolute Deviation (MAD)18
Skewness0.65047147
Sum678147
Variance794.96389
MonotonicityNot monotonic
2024-05-02T17:56:37.814405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
54 3116
28.4%
90 2590
23.6%
36 2327
21.2%
72 1218
 
11.1%
18 672
 
6.1%
108 325
 
3.0%
126 289
 
2.6%
9 141
 
1.3%
27 136
 
1.2%
180 71
 
0.6%
Other values (25) 85
 
0.8%
ValueCountFrequency (%)
9 141
 
1.3%
12 2
 
< 0.1%
15 3
 
< 0.1%
18 672
6.1%
20 4
 
< 0.1%
21 2
 
< 0.1%
23 4
 
< 0.1%
24 2
 
< 0.1%
27 136
 
1.2%
30 7
 
0.1%
ValueCountFrequency (%)
180 71
 
0.6%
144 2
 
< 0.1%
126 289
 
2.6%
111 1
 
< 0.1%
108 325
 
3.0%
104 1
 
< 0.1%
101 1
 
< 0.1%
90 2590
23.6%
87 1
 
< 0.1%
86 1
 
< 0.1%

loan_initial_amount
Real number (ℝ)

Distinct232
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6091.551
Minimum1680
Maximum93600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.8 KiB
2024-05-02T17:56:38.174465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1680
5-th percentile1920
Q13456
median4992
Q37200
95-th percentile13440
Maximum93600
Range91920
Interquartile range (IQR)3744

Descriptive statistics

Standard deviation3961.0339
Coefficient of variation (CV)0.65025047
Kurtosis34.530375
Mean6091.551
Median Absolute Deviation (MAD)2112
Skewness3.2509539
Sum66824315
Variance15689790
MonotonicityNot monotonic
2024-05-02T17:56:38.541238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4800 1106
 
10.1%
7200 801
 
7.3%
2400 710
 
6.5%
5760 569
 
5.2%
3840 558
 
5.1%
2880 458
 
4.2%
1680 427
 
3.9%
3360 412
 
3.8%
4320 405
 
3.7%
9600 400
 
3.6%
Other values (222) 5124
46.7%
ValueCountFrequency (%)
1680 427
3.9%
1728 2
 
< 0.1%
1752 1
 
< 0.1%
1776 4
 
< 0.1%
1800 1
 
< 0.1%
1824 2
 
< 0.1%
1872 1
 
< 0.1%
1920 276
2.5%
1992 1
 
< 0.1%
2016 11
 
0.1%
ValueCountFrequency (%)
93600 1
 
< 0.1%
69120 1
 
< 0.1%
48000 2
< 0.1%
42240 1
 
< 0.1%
40800 1
 
< 0.1%
33600 1
 
< 0.1%
30720 1
 
< 0.1%
28800 4
< 0.1%
27360 1
 
< 0.1%
26880 1
 
< 0.1%

loan_to_value_ratio
Real number (ℝ)

Distinct1120
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62581684
Minimum0.040740741
Maximum8.4782609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.8 KiB
2024-05-02T17:56:38.917500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.040740741
5-th percentile0.26315789
Q10.5
median0.66666667
Q30.78947368
95-th percentile0.875
Maximum8.4782609
Range8.4375201
Interquartile range (IQR)0.28947368

Descriptive statistics

Standard deviation0.2202144
Coefficient of variation (CV)0.35188315
Kurtosis198.06121
Mean0.62581684
Median Absolute Deviation (MAD)0.13333333
Skewness6.1284786
Sum6865.2107
Variance0.048494382
MonotonicityNot monotonic
2024-05-02T17:56:39.282558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 963
 
8.8%
0.5 442
 
4.0%
0.75 362
 
3.3%
0.6 308
 
2.8%
0.9 307
 
2.8%
0.6666666667 301
 
2.7%
0.625 260
 
2.4%
0.8333333333 227
 
2.1%
0.7142857143 224
 
2.0%
0.7692307692 169
 
1.5%
Other values (1110) 7407
67.5%
ValueCountFrequency (%)
0.04074074074 1
< 0.1%
0.04545454545 1
< 0.1%
0.05974025974 1
< 0.1%
0.0618556701 1
< 0.1%
0.06538461538 1
< 0.1%
0.06981132075 1
< 0.1%
0.07142857143 1
< 0.1%
0.07692307692 1
< 0.1%
0.07894736842 1
< 0.1%
0.0793814433 1
< 0.1%
ValueCountFrequency (%)
8.47826087 1
< 0.1%
5.833333333 1
< 0.1%
5.333333333 1
< 0.1%
3.888888889 1
< 0.1%
1.962560974 1
< 0.1%
1.597312722 1
< 0.1%
1.301052632 1
< 0.1%
1.2 1
< 0.1%
1.175 1
< 0.1%
1.061415546 1
< 0.1%

annual_percentage_rate
Real number (ℝ)

Distinct7182
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.399138
Minimum19.841096
Maximum54.501862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.8 KiB
2024-05-02T17:56:39.627518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum19.841096
5-th percentile29.440909
Q134.013923
median35.915354
Q336.958098
95-th percentile39.422817
Maximum54.501862
Range34.660766
Interquartile range (IQR)2.944175

Descriptive statistics

Standard deviation3.0910412
Coefficient of variation (CV)0.087319675
Kurtosis3.61393
Mean35.399138
Median Absolute Deviation (MAD)1.2327729
Skewness-0.41124843
Sum388328.55
Variance9.554536
MonotonicityNot monotonic
2024-05-02T17:56:40.013657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.11805625 24
 
0.2%
38.27843974 21
 
0.2%
37.06229324 18
 
0.2%
36.412265 17
 
0.2%
36.37183013 15
 
0.1%
37.09016621 15
 
0.1%
36.29131896 14
 
0.1%
35.48274939 12
 
0.1%
37.28588077 12
 
0.1%
37.01369954 12
 
0.1%
Other values (7172) 10810
98.5%
ValueCountFrequency (%)
19.84109589 1
< 0.1%
20.4245887 1
< 0.1%
20.5759692 1
< 0.1%
20.65722462 1
< 0.1%
20.67344486 1
< 0.1%
20.68876102 1
< 0.1%
20.77566325 1
< 0.1%
20.78718153 1
< 0.1%
20.81876534 1
< 0.1%
20.83961846 1
< 0.1%
ValueCountFrequency (%)
54.50186212 1
< 0.1%
54.0241842 1
< 0.1%
52.3438013 1
< 0.1%
52.06081289 1
< 0.1%
52.0160863 1
< 0.1%
51.83446821 1
< 0.1%
51.13353812 1
< 0.1%
50.99258106 1
< 0.1%
50.98807099 2
< 0.1%
50.18068113 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size688.3 KiB
3.374
7079 
2.6710832771
3448 
2.5305
 
443

Length

Max length12
Median length5
Mean length7.2405652
Min length5

Characters and Unicode

Total characters79429
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.374
2nd row3.374
3rd row3.374
4th row3.374
5th row3.374

Common Values

ValueCountFrequency (%)
3.374 7079
64.5%
2.6710832771 3448
31.4%
2.5305 443
 
4.0%

Length

2024-05-02T17:56:40.391778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-02T17:56:40.663184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3.374 7079
64.5%
2.6710832771 3448
31.4%
2.5305 443
 
4.0%

Most occurring characters

ValueCountFrequency (%)
3 18049
22.7%
7 17423
21.9%
. 10970
13.8%
2 7339
9.2%
4 7079
 
8.9%
1 6896
 
8.7%
0 3891
 
4.9%
6 3448
 
4.3%
8 3448
 
4.3%
5 886
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79429
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 18049
22.7%
7 17423
21.9%
. 10970
13.8%
2 7339
9.2%
4 7079
 
8.9%
1 6896
 
8.7%
0 3891
 
4.9%
6 3448
 
4.3%
8 3448
 
4.3%
5 886
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79429
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 18049
22.7%
7 17423
21.9%
. 10970
13.8%
2 7339
9.2%
4 7079
 
8.9%
1 6896
 
8.7%
0 3891
 
4.9%
6 3448
 
4.3%
8 3448
 
4.3%
5 886
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79429
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 18049
22.7%
7 17423
21.9%
. 10970
13.8%
2 7339
9.2%
4 7079
 
8.9%
1 6896
 
8.7%
0 3891
 
4.9%
6 3448
 
4.3%
8 3448
 
4.3%
5 886
 
1.1%

region
Categorical

MISSING 

Distinct12
Distinct (%)0.1%
Missing1090
Missing (%)9.9%
Memory size687.3 KiB
region 6
3728 
region 5
2055 
region 8
911 
region 3
737 
region 7
642 
Other values (7)
1807 

Length

Max length9
Median length8
Mean length8.0452429
Min length8

Characters and Unicode

Total characters79487
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregion 9
2nd rowregion 2
3rd rowregion 6
4th rowregion 2
5th rowregion 5

Common Values

ValueCountFrequency (%)
region 6 3728
34.0%
region 5 2055
18.7%
region 8 911
 
8.3%
region 3 737
 
6.7%
region 7 642
 
5.9%
region 9 561
 
5.1%
region 2 537
 
4.9%
region 10 400
 
3.6%
region 1 213
 
1.9%
region 4 49
 
0.4%
Other values (2) 47
 
0.4%
(Missing) 1090
 
9.9%

Length

2024-05-02T17:56:40.944490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
region 9880
50.0%
6 3728
 
18.9%
5 2055
 
10.4%
8 911
 
4.6%
3 737
 
3.7%
7 642
 
3.2%
9 561
 
2.8%
2 537
 
2.7%
10 400
 
2.0%
1 213
 
1.1%
Other values (3) 96
 
0.5%

Most occurring characters

ValueCountFrequency (%)
r 9880
12.4%
e 9880
12.4%
g 9880
12.4%
i 9880
12.4%
o 9880
12.4%
n 9880
12.4%
9880
12.4%
6 3728
 
4.7%
5 2055
 
2.6%
8 911
 
1.1%
Other values (7) 3633
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79487
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 9880
12.4%
e 9880
12.4%
g 9880
12.4%
i 9880
12.4%
o 9880
12.4%
n 9880
12.4%
9880
12.4%
6 3728
 
4.7%
5 2055
 
2.6%
8 911
 
1.1%
Other values (7) 3633
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79487
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 9880
12.4%
e 9880
12.4%
g 9880
12.4%
i 9880
12.4%
o 9880
12.4%
n 9880
12.4%
9880
12.4%
6 3728
 
4.7%
5 2055
 
2.6%
8 911
 
1.1%
Other values (7) 3633
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79487
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 9880
12.4%
e 9880
12.4%
g 9880
12.4%
i 9880
12.4%
o 9880
12.4%
n 9880
12.4%
9880
12.4%
6 3728
 
4.7%
5 2055
 
2.6%
8 911
 
1.1%
Other values (7) 3633
 
4.6%

branch
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size696.5 KiB
branch 5
3697 
branch 6
2274 
branch 3
1964 
branch 2
1501 
branch 7
641 
Other values (2)
893 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters87760
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbranch 3
2nd rowbranch 3
3rd rowbranch 5
4th rowbranch 3
5th rowbranch 2

Common Values

ValueCountFrequency (%)
branch 5 3697
33.7%
branch 6 2274
20.7%
branch 3 1964
17.9%
branch 2 1501
13.7%
branch 7 641
 
5.8%
branch 4 552
 
5.0%
branch 1 341
 
3.1%

Length

2024-05-02T17:56:41.240334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-02T17:56:41.517713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
branch 10970
50.0%
5 3697
 
16.9%
6 2274
 
10.4%
3 1964
 
9.0%
2 1501
 
6.8%
7 641
 
2.9%
4 552
 
2.5%
1 341
 
1.6%

Most occurring characters

ValueCountFrequency (%)
b 10970
12.5%
r 10970
12.5%
a 10970
12.5%
n 10970
12.5%
c 10970
12.5%
h 10970
12.5%
10970
12.5%
5 3697
 
4.2%
6 2274
 
2.6%
3 1964
 
2.2%
Other values (4) 3035
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 10970
12.5%
r 10970
12.5%
a 10970
12.5%
n 10970
12.5%
c 10970
12.5%
h 10970
12.5%
10970
12.5%
5 3697
 
4.2%
6 2274
 
2.6%
3 1964
 
2.2%
Other values (4) 3035
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 10970
12.5%
r 10970
12.5%
a 10970
12.5%
n 10970
12.5%
c 10970
12.5%
h 10970
12.5%
10970
12.5%
5 3697
 
4.2%
6 2274
 
2.6%
3 1964
 
2.2%
Other values (4) 3035
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 10970
12.5%
r 10970
12.5%
a 10970
12.5%
n 10970
12.5%
c 10970
12.5%
h 10970
12.5%
10970
12.5%
5 3697
 
4.2%
6 2274
 
2.6%
3 1964
 
2.2%
Other values (4) 3035
 
3.5%

client_gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size656.9 KiB
male
9261 
female
1709 

Length

Max length6
Median length4
Mean length4.311577
Min length4

Characters and Unicode

Total characters47298
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowmale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male 9261
84.4%
female 1709
 
15.6%

Length

2024-05-02T17:56:41.884670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-02T17:56:42.157828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
male 9261
84.4%
female 1709
 
15.6%

Most occurring characters

ValueCountFrequency (%)
e 12679
26.8%
m 10970
23.2%
a 10970
23.2%
l 10970
23.2%
f 1709
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47298
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 12679
26.8%
m 10970
23.2%
a 10970
23.2%
l 10970
23.2%
f 1709
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47298
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 12679
26.8%
m 10970
23.2%
a 10970
23.2%
l 10970
23.2%
f 1709
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47298
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 12679
26.8%
m 10970
23.2%
a 10970
23.2%
l 10970
23.2%
f 1709
 
3.6%

income
Real number (ℝ)

ZEROS 

Distinct383
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1143.7324
Minimum0
Maximum52800
Zeros2770
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size85.8 KiB
2024-05-02T17:56:42.473013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median960
Q31440
95-th percentile2880
Maximum52800
Range52800
Interquartile range (IQR)1440

Descriptive statistics

Standard deviation1573.4205
Coefficient of variation (CV)1.3756894
Kurtosis232.53274
Mean1143.7324
Median Absolute Deviation (MAD)480
Skewness10.698369
Sum12546744
Variance2475652
MonotonicityNot monotonic
2024-05-02T17:56:42.923197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2770
25.3%
1440 1156
10.5%
1200 1097
 
10.0%
960 1040
 
9.5%
720 728
 
6.6%
1920 591
 
5.4%
2400 424
 
3.9%
1680 319
 
2.9%
864 250
 
2.3%
576 129
 
1.2%
Other values (373) 2466
22.5%
ValueCountFrequency (%)
0 2770
25.3%
1 5
 
< 0.1%
2 1
 
< 0.1%
106 1
 
< 0.1%
120 3
 
< 0.1%
144 1
 
< 0.1%
149 1
 
< 0.1%
216 2
 
< 0.1%
221 4
 
< 0.1%
223 1
 
< 0.1%
ValueCountFrequency (%)
52800 1
 
< 0.1%
43200 1
 
< 0.1%
38400 1
 
< 0.1%
33600 1
 
< 0.1%
28800 1
 
< 0.1%
24000 1
 
< 0.1%
21120 1
 
< 0.1%
19200 7
0.1%
16320 1
 
< 0.1%
14400 13
0.1%

vehicle_production_year
Real number (ℝ)

Distinct35
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.0438
Minimum1982
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.8 KiB
2024-05-02T17:56:43.317840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1982
5-th percentile1996
Q11999
median2003
Q32006
95-th percentile2012
Maximum2020
Range38
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.9220185
Coefficient of variation (CV)0.0024572695
Kurtosis-0.37182747
Mean2003.0438
Median Absolute Deviation (MAD)4
Skewness0.29169417
Sum21973391
Variance24.226266
MonotonicityNot monotonic
2024-05-02T17:56:43.703219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1999 959
 
8.7%
1998 917
 
8.4%
2000 796
 
7.3%
2005 777
 
7.1%
2002 736
 
6.7%
2004 729
 
6.6%
2006 717
 
6.5%
2003 711
 
6.5%
2001 667
 
6.1%
2007 657
 
6.0%
Other values (25) 3304
30.1%
ValueCountFrequency (%)
1982 1
 
< 0.1%
1985 1
 
< 0.1%
1986 1
 
< 0.1%
1989 2
 
< 0.1%
1990 3
 
< 0.1%
1991 12
 
0.1%
1992 27
 
0.2%
1993 42
 
0.4%
1994 137
1.2%
1995 256
2.3%
ValueCountFrequency (%)
2020 1
 
< 0.1%
2019 2
 
< 0.1%
2018 12
 
0.1%
2017 24
 
0.2%
2016 32
 
0.3%
2015 50
 
0.5%
2014 108
 
1.0%
2013 139
1.3%
2012 231
2.1%
2011 326
3.0%
Distinct489
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10286.784
Minimum1104
Maximum168000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.8 KiB
2024-05-02T17:56:44.085981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1104
5-th percentile4320
Q16240
median8160
Q312212
95-th percentile23040
Maximum168000
Range166896
Interquartile range (IQR)5972

Descriptive statistics

Standard deviation7192.8341
Coefficient of variation (CV)0.69923056
Kurtosis49.444218
Mean10286.784
Median Absolute Deviation (MAD)2400
Skewness4.5249967
Sum1.1284602 × 108
Variance51736862
MonotonicityNot monotonic
2024-05-02T17:56:44.482202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5760 693
 
6.3%
7200 658
 
6.0%
7680 607
 
5.5%
6720 582
 
5.3%
5280 557
 
5.1%
8160 490
 
4.5%
6240 475
 
4.3%
9600 427
 
3.9%
8640 410
 
3.7%
4800 402
 
3.7%
Other values (479) 5669
51.7%
ValueCountFrequency (%)
1104 1
 
< 0.1%
1440 1
 
< 0.1%
1872 1
 
< 0.1%
1920 3
 
< 0.1%
2160 6
 
0.1%
2246 1
 
< 0.1%
2304 2
 
< 0.1%
2385 1
 
< 0.1%
2400 38
0.3%
2419 1
 
< 0.1%
ValueCountFrequency (%)
168000 1
 
< 0.1%
144000 1
 
< 0.1%
96000 2
 
< 0.1%
91200 2
 
< 0.1%
86400 2
 
< 0.1%
72000 1
 
< 0.1%
67200 5
< 0.1%
64800 2
 
< 0.1%
62400 6
0.1%
60000 2
 
< 0.1%

age
Real number (ℝ)

Distinct57
Distinct (%)0.5%
Missing22
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean37.719401
Minimum18
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.8 KiB
2024-05-02T17:56:44.884605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q129
median35
Q344
95-th percentile61
Maximum78
Range60
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.298195
Coefficient of variation (CV)0.29953274
Kurtosis0.036227506
Mean37.719401
Median Absolute Deviation (MAD)7
Skewness0.87510596
Sum412952
Variance127.64922
MonotonicityNot monotonic
2024-05-02T17:56:45.304211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 519
 
4.7%
31 500
 
4.6%
29 485
 
4.4%
33 462
 
4.2%
28 453
 
4.1%
27 448
 
4.1%
34 427
 
3.9%
25 406
 
3.7%
32 402
 
3.7%
35 396
 
3.6%
Other values (47) 6450
58.8%
ValueCountFrequency (%)
18 1
 
< 0.1%
19 4
 
< 0.1%
20 14
 
0.1%
21 56
 
0.5%
22 103
 
0.9%
23 219
2.0%
24 363
3.3%
25 406
3.7%
26 395
3.6%
27 448
4.1%
ValueCountFrequency (%)
78 2
 
< 0.1%
74 1
 
< 0.1%
72 9
 
0.1%
71 16
 
0.1%
70 29
0.3%
69 19
 
0.2%
68 40
0.4%
67 43
0.4%
66 42
0.4%
65 60
0.5%

had_car_loan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size621.5 KiB
0
9336 
1
1634 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10970
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9336
85.1%
1 1634
 
14.9%

Length

2024-05-02T17:56:45.653875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-02T17:56:45.908429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 9336
85.1%
1 1634
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 9336
85.1%
1 1634
 
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9336
85.1%
1 1634
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9336
85.1%
1 1634
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9336
85.1%
1 1634
 
14.9%

had_other_loans
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size621.5 KiB
0
9610 
1
1360 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10970
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9610
87.6%
1 1360
 
12.4%

Length

2024-05-02T17:56:46.182048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-02T17:56:46.413228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 9610
87.6%
1 1360
 
12.4%

Most occurring characters

ValueCountFrequency (%)
0 9610
87.6%
1 1360
 
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9610
87.6%
1 1360
 
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9610
87.6%
1 1360
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9610
87.6%
1 1360
 
12.4%

Interactions

2024-05-02T17:56:30.002416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:05.472766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:08.332062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:10.801435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:13.361185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:15.954154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:18.484212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:21.606846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:24.178581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:27.096026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:30.340760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:05.765246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:08.563654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:11.076032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:13.611768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:16.188761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:18.745939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:21.852724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:24.463596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:27.396220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:30.571858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:05.986157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:08.777659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:11.309331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:13.891684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:16.407464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:19.012012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:22.086334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:24.717658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:27.710160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:30.862937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:06.230873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:09.014587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:11.560201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:14.132407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:16.680536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:19.273331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:22.346738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:24.992335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:28.005338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:31.112044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:06.471546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:09.230582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:11.812434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:14.347507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:16.924638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:19.568098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:22.582139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:25.255251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:28.281960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:31.343304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:06.699664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:09.453683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:12.044227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:14.580306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:17.162209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:20.217161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:22.824364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:25.495066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:28.531610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:31.600999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:06.950756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:09.713522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:12.310658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:14.841245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:17.414421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:20.483531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:23.102798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:25.874933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:28.817688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:31.838600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:07.490936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:09.947775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:12.558560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:15.077872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:17.664313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:20.760545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:23.352134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:26.159035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:29.117922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:32.103803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:07.761087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:10.251473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:12.832330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:15.433102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:17.934656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:21.047838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:23.630693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:26.482326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:29.395669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:32.383896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:08.092654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:10.547924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:13.101914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:15.714616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:18.235948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:21.343213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:23.920994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:26.804302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-02T17:56:29.714016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-02T17:56:32.776486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-02T17:56:33.489502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-02T17:56:33.893713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

clientidclient_typeratioloan_typeloan_initial_termloan_initial_amountloan_to_value_ratioannual_percentage_ratemonthly_interest_rateregionbranchclient_genderincomevehicle_production_yearvehicle_initial_assessment_valueagehad_car_loanhad_other_loans
00default1.166667leaseback3619200.20000035.8757633.374region 9branch 3male12142002960037.000
11default1.027778leaseback5450400.77777836.0797493.374NaNbranch 3male01995648036.000
22default0.916667leasing1824000.45454542.2920283.374region 2branch 5male7201999528057.000
33default0.916667leaseback3693600.88636435.5800553.374NaNbranch 3female020001056065.000
44default0.888889leasing5457600.75000037.0901663.374region 6branch 2male02008768035.000
55default0.875000leaseback3624000.33333339.5865393.374NaNbranch 3male01998720032.010
66default0.833333leaseback3621600.26470637.9009813.374region 2branch 5male02012816033.001
77default0.833333leaseback18129600.79411841.0836973.374region 5branch 5male1920020091632036.000
88default0.833333leaseback3638400.61538537.5385823.374region 6branch 2female9601996624036.000
99default0.833333leaseback1821600.37500036.8114463.374region 6branch 3male01998576029.000
clientidclient_typeratioloan_typeloan_initial_termloan_initial_amountloan_to_value_ratioannual_percentage_ratemonthly_interest_rateregionbranchclient_genderincomevehicle_production_yearvehicle_initial_assessment_valueagehad_car_loanhad_other_loans
1096010960regularNaNleaseback10852800.68750035.2240003.374NaNbranch 3male01997768030.000
1096110961regularNaNleaseback90148800.77500035.8801663.374NaNbranch 3female020091920025.000
1096210962regularNaNleaseback7238400.41025636.7020573.374NaNbranch 3male02003936045.000
1096310963regularNaNleaseback9033600.63636436.0870203.374NaNbranch 3male02003528040.000
1096410964regularNaNleaseback9062400.74285735.0532303.374NaNbranch 3male7202001840052.000
1096510965regularNaNleaseback90144000.62500036.2559343.374NaNbranch 3male020042304062.000
1096610966regularNaNleaseback9043200.56250036.0489643.374NaNbranch 3male01994768035.000
1096710967regularNaNleaseback9043200.60000035.8996663.374NaNbranch 3male01998720024.000
1096810968regularNaNleaseback7228800.28571437.5473473.374NaNbranch 3male020041008033.000
1096910969regularNaNleaseback9072000.45652236.3718303.374NaNbranch 3male020001577149.000